Our proposed LPT involves a min-max optimization problem, which is analogous to that in adversarial learning.
Adversarial learning [1]} has been widely applied to 1) data generation [1]}, [3]} where a discriminator tries to distinguish between generated images and real images and a generator is trained to generate realistic data by making such a discrimination difficult to achieve; 2) domain adaptation [4]} where a discriminator tries to differentiate between source images and target images while the feature learner learns representations which make such a discrimination unachievable; 3) adversarial attack and defence [5]} where an attacker adds small perturbations to the input data to alter the prediction outcome and the defender trains the model in a way that the prediction outcome remains the same given perturbed inputs. Different from these existing works, in our work, a tester aims to create harder tests to “fail" the learner while the learner learns to “pass" however hard tests created by the tester. [6]} proposed to use an adversarial examiner to identify the weakness of a trained model. Our work differs from this one in that we progressively re-train a learner model based on how it performs on the tests dynamically created by a tester model while the learner model in [6]} is fixed and not affected by the examination results.
